City University of New York (CUNY) CUNY Academic Works International Conference on Hydroinformatics 2014 Rainfall Variability Analysis In The Nira River Basin Using Multi- Model GCM Ensemble Asmita Ramkrishna Murumkar Dhyan Singh Arya How does access to this work benefit ou?y Let us know! More information about this work at: https://academicworks.cuny.edu/cc_conf_hic/209 Discover additional works at: https://academicworks.cuny.edu This work is made publicly available by the City University of New York (CUNY). Contact: [email protected] 11th International Conference on Hydroinformatics HIC 2014, New York City, USA RAINFALL VARIABILITY ANALYSIS IN THE NIRA RIVER BASIN USING MULTI-MODEL GCM ENSEMBLE A. R. MURUMKAR (1), D.S. ARYA (1) (1): Department of Hydrology, IIT Roorkee, Roorkee 247 667, Uttarakhand, India Observed daily rainfall data during baseline period i.e. 1961-1990 of four rain gauge stations namely; Akluj, Baramati, Bhor and Malsiras located in the Nira River basin in Central India were analyzed to study the impact of climate change on rainfall. LARS-WG incorporating 15 GCM‘s from the CMIP3 predictions for A1B, A2 and B1 emission scenarios was used to statistically downscale the daily rainfall data during three time spans centred at 2020‘s, 2055‘s and 2090‘s. Uncertainty in GCMs rainfall predictions was analyzed on monthly, seasonal and annual scales. Kolmogorov-Smirnov test, t-test, and Fisher test have shown average to good performance during synthetic rainfall data generation for all the stations. The analysis of the data shows that the uncertainty in the prediction increases with the timescale. Also, the variability in the predictions is smaller in annual values followed by seasonal and monthly values. Maximum uncertainty is observed in A2 scenario, followed by A1B, and B1 Scenarios. Monsoon months show minimum uncertainty in all the scenarios. The rainfall of December, March, April and May months are expected to increase in first two spans while expected to decrease in the last time span 2080 -2099 under all the scenarios. The monsoon month‘s rainfall is expected to increase slightly in the future for all the scenarios. Baramati shows maximum increase in annual rainfall for all scenarios while rainfall at Malsiras is expected to decrease only during third time span for all three scenarios. KEYWORD: Downscaling, Climate Change, Uncertainty, Ensemble, GCM, CMIP3 INTRODUCTION Global Climate Models (GCMs) are the primary tools for understanding how the global climate may change in the future. The hydrological processes typically occur on finer scales [1]. In particular, GCMs cannot resolve circulation patterns leading to hydrological extreme events [2]. Hence, to reliably assess hydrological impacts of climate change, higher resolution scenarios are required for the most relevant meteorological variables. Downscaling technique attempts to resolve scale discrepancy between higher resolution climate change scenarios and the resolution required for impact assessment. It is based on the assumption that large scale weather exhibits a strong influence on local scale weather; but, in general, disregards any reverse effects from local scales upon global scales. Two approaches of downscaling are: dynamical downscaling, and statistical downscaling. Dynamical downscaling nests a regional climate model (RCM) into the GCM to represent the atmospheric physics with a higher grid box resolution within a limited area of interest. Statistical downscaling establishes statistical links between larger scale weather and observed local scale weather. Stochastic weather generator is one of the statistical downscaling tools, widely used by many researchers world-wide [3][4][5][6]. Some stochastic weather generators may be site- specific, i.e., they generate weather time-series for a single site; while others may be spatially distributed, i.e., they generate weather for a number of locations simultaneously, and reflect the spatial correlation of the different climate variables [7][8]. LARS-WG (Long Ashton Research Station Weather Generator) model, a stochastic weather generator, has been tested in diverse climates and demonstrated a good performance in reproduction of various weather statistics including extreme weather events [9][10]. The latest report of the Intergovernmental Panel on Climate Change (IPCC) has presented long-term projections of climate change into the next century. Atmospheric evolution of that prediction is chaotic, i.e. sensitive to initial-condition uncertainty. A standard approach to reduce climate noise in model predictions is used by averaging the ensemble of forecasts initiated from different initial conditions [11]. The performance of multi-model climate predictions produced by three GCMs and found that the multi-model approach offers a systematic improvement when using the ensemble to produce probabilistic forecasts [12]. The multi-model ensemble improves skill only marginally when verifying the ensemble mean, however. On the other hand, found an apparent systematic improvement in mean square error for a multi-model forecast over that of the individual model forecasts [13]. The aim of this study was to assess the impact of climate change on rainfall at local scale by predicting future ensemble rainfall using 15 GCMS in three different scenarios with the help of LARS-WG statistical downscaling tool. Furthermore, the manuscript/paper presents an analysis of monthly, seasonal and annual changes in rainfall pattern in the Nira River Basin, Maharashtra (India). MATERIAL AND METHODS Study Area and Data The Nira catchment is a sub-basin of the Bhima watershed in the state of Maharashtra (India), and covers an area of 6900 km2. The river flows to the southeast, over the plains of the Deccan Plateau, a fertile agricultural area with densely populated riverbanks [14]. The Nira catchment and locations of four rain gauge stations are shown in Figure 1. Daily rainfall data (1961-1990) of four stations were obtained from the India Meteorological Department (IMD), Pune. Methodology The process of generating synthetic weather data by using LARS-WG can be divided into three distinct steps: 1. Model Calibration - SITE ANALYSIS - observed weather data are analyzed to determine their statistical characteristics. This information is stored in two parameter files. 2. Model Validation - QTEST - the statistical characteristics of the observed and synthetic weather data are analyzed to determine if there are any statistically- significant differences. 3. Generation of Synthetic Weather Data - GENERATOR - the parameter files derived from observed weather data during the model calibration process are used to generate synthetic weather data having the same statistical characteristics as the original observed data, but differing on a day-to-day basis. Synthetic data corresponding to a particular climate change scenario may also be generated by applying global climate model-derived changes in precipitation, temperature and solar radiation to the LARS- WG parameter files. Figure. 1 Nira Catchment and locations of four rainfall station LARS enlists the 15 GCMs (Coupled Atmosphere-Ocean models) incorporated in LARS-WG [15] with varying resolution from 1.10 x1.10 to 40 x 50. The weather generator was used to forecast rainfall data of four stations (Akluj, Baramati, Bhor and Malsiras) for three emission scenarios namely; A1B, A2, and B1 using these GCMs at each stations. The outputs from these GCMs involved baseline period corresponding to 1960-1990 and three future time spans i.e. 2011-2030, 2045-206, and 2080-2099 [15]. Each year was divided into quarters that represented four seasons, viz. DJF (December 1st of previous year through February 28th), MAM (March 1st through May 31st), JJA (June 1st through August 31st) and SON (September 1st through November 30th). The Kolmogorov-Smirnov (K-S), goodness-of-fit test was used to compare the probability distributions of lengths of wet series (rainfall > 0 mm) and dry series (no rainfall), respectively for each season as well as distributions of daily rainfall for monthly data. The monthly mean of the observed series with that of the synthetic series were compared using t-test. The Fisher F-test usually measures the inter-annual variability of observed and generated monthly rainfall means. The p-value associated with the t-test indicates the probability that monthly mean rainfalls are derived from the same population. In this study p- values less than 0.05 were considered as indicators of the likelihood of a substantial difference between the ‗true‘ and simulated climate for that particular variable. To evaluate the change in daily rainfall due to climate change, we compared the annual mean rainfall, seasonal mean rainfall and monthly mean rainfall of both observed and downscaled rainfall data. The relative changes in these variables were calculated using equation 1. Change = ((Future – Current)/Current)*100 (1) Calibration and Validation First, the daily rainfall data (nearly 30 years) were imported into the weather generating model for analysis and computation of the statistical properties, viz. distribution types for the lengths of wet and dry series, daily rainfall distributions for each month, and monthly means and standard deviations. Secondly, the weather generating models were calibrated using the computed properties. After calibration, random seed values were selected at each station, and each model was subjected to different numbers of runs over a length of 300 years for the generation